In the present paper, we exploit the results of a recent work on multisubject book classification by extending its application to book collections written in languages other than English, specifically in Greek. The proposed classification method consists of utilizing the word statistics in the book’s Table of Content as well as in a controlled subject-term vocabulary, in combination with the Latent Dirichlet Allocation (LDA), a well-known machine learning technique for discovering hidden topics in a corpus of documents. The proposed method was theoretically formulated and validated through an extensive set of experiments performed on Springer’s English language e-book collection. Now, the classification method is applied on book collections written in Greek: a set of about fifty thousand academic books, provided by commercial publishers through the EVDOXUS service, and a more limited collection of digital books publicly available with open licenses (the KALLIPOS collection). The derived qualitative and quantitative results show the language-neutral applicability of the proposed approach, with the Latent Dirichlet Allocation method, combined with simple Bayesian inference, also being highly effective in analysing Greek language collections. Upon examining traditional metrics such as precision and recall, it is evident that their values converge and surpass a score of 0.82 when classifying unknown documents in Greek across 26 different subjects. This confirms the efficacy of the suggested approach and paves the way for the application of the proposed classification method to multilingual collections, provided that the vocabulary of the subject terms is available in other languages of interest. The availability of common Natural Language Processing tools, as for example stemmers, lemmatizers, common-word filters, required for document preprocessing, is taken for granted in all modern Natural Language Processing programming platforms.